Existing literature sutdies the contract completeness

• More complete contracts have lower ex-post opportunistic behavior but bears higher ex-ante writing costs (e.g., Crocker and Reynolds 1993).

Existing literature usually relies on a small sample from one industry

• Crocker and Reynolds (1993) examine 44 contracts in the U.S. defense industry;
• Saussier (2000) examines 29 contracts in the French coal industry;
• Anderson and Dekker (2005) examine 858 contracts in the information technologies (IT) industry;
• Aubert et al. (2017) use 173 survey observations regarding outsourcing contracts in the IT industry;
• Krishnan and Mani (2020) analyze 455 strategic outsourcing contracts.

Contract contingency is a better perspective to study contract completeness

• Existing literature often assumes that more actions included in a contract (or a longer contract) indicate more complete contracts.
• As opposed to contract completeness, this study focuses on well-defined contract contingencies.

“clause comprehensiveness” → “risk response capability”

Writing cost perspective

• In the traditional contracting theory framework, writing contingencies is costless (Arrow 1964).
• With bounded rationality and opportunism:
  • thinking about possible outcomes;
  • seeing through their implications;
  • designing (and bargaining) related actions;
  • and writing them in a contract formally are all costly activities (Dye 1985).

• Dye (1985) argues that writing cost is propotional to the number of contingencies.
• As uncertainty grows, too cost-ineffective to:
  • contemplate all possible outcomes, each with a small likelihood,
  • bargain and negotiate corresponding actions.

graph TD
    classDef dashed fill:none,stroke-dasharray: 5 5;
    subgraph "High Uncertainty"
        direction TB
        T0h2[T=0] -->|20%| A1h2[Action 1]
        T0h2 -->|5%| A2h2[Action 2]
        T0h2 -->|5%| A3h2[Action 3]
        T0h2 -.-> Ax1[Action X1]
        T0h2 -.-> Ax2[Action X2]
        Ax1:::dashed
        Ax2:::dashed
    end

    subgraph "Low Uncertainty"
        direction TB
        T0h1[T=0] -->|60%| A1h1[Action 1]
        T0h1 -->|20%| A2h1[Action 2]
        T0h1 -->|20%| A3h1[Action 3]
    end

• High uncertainty → fewer contingencies
• Since with fewer contigencies, parties have to amend actions upon observing the outcome realization.

Dynamic contracting models

• Conditional on the written contingency, the cost of amending actions ex-post is much lower than re-negotiating an entirely new contract (Battigalli and Maggi, 2008).
• Alternatively, actions drafted roughly in the first place often serve as good reference points to facilitate future amendments (Hart and Moore 2008).
  • Example: a “normal” shipping disruption-related contingency helps serve as a good reference point for determining the period of further delay under more “severe” shipping disruptions.

• Roughly drafted contingencies lower ex-ante writing costs while facilitating less costly future amendments (Tirole, 2009).
• High uncertainty: limited number of roughly drafted clauses + ex-post amendments(Tirole, 2009).
• High uncertainty → more contingencies (roughly drafted)

Price protection view

• Williamson (1985): If the parties could ex-ante negotiate payoffs to provide protection, a contract with few contingencies would still be deployed under high uncertainty.
• Grossman and Hart (1986): The party with higher exposure retains asset ownership and bears the residual risks.
• Maskin and Tirole (1999): Uncertainty does not matter if the parties can probabilistically forecast future payoffs.
• No significant relationship between uncertainty and contingency

Sample and measurement of contigencies

• Sample period: 1996 - 2021

• Identify relevant subsections: develop an algorithm to split a contract into subsections.
• Group subsections into “topics” (i.e., delivery, warranty): using LDA model.
• Identify actions and contingencies on topic level: a topic is designated a “contingency topic” if it contains at least one contingency clause.
• Aggregate actions and contingencies to the contract level:
• ContingencyR = contingency topics / action topics

Measurement of general uncertainty

• Industry earnings volatility
  • industry-level average earnings volatility
• Industry cash flow volatility
  • industry-level average cash flow volatility
• Industry return volatility
  • industry-level average stock return volatility

Innovation-related uncertainty and contingency

• Contracts for innovation are inherently incomplete due to either high writing costs or an inability to address unlikely outcomes (Williamson, 1985).
• Innovation-related contingencies
  • ContingencyR = innovation-related contingency topics / innovation-related action topics
• Innovation uncertainty
  • industry-level average R&D intensity
  • industry-level average originality of all patents
  • industry-level fraction of firms using trade secrecy

Descriptive statistics

Research design

\[ Contingency_c = \beta \ IndustryUncertainty_{j,t} + X_{i,j,t - 1} + \gamma_t + \nu_j + \epsilon_c \]
where c, i, j, and t index contract, firm, industry, and year.

• X includes
  • industry ROA, firm ROA, firm gross profit margin, firm sales, firm assets, number of firm segments, firm age, industry average of market-to-book ratio, firm leverage ratios, firm trade credit, same state, private.
• Control year and industry FEs.

Main results

Identification strategy

• General uncertainty shocks: Financial crisis and COVID-19 pandemic \[ \begin{aligned} \text{ContingencyR}_{c} &= \beta_{1}\text{PostFinCrisis}_{t}+\beta_{2}\text{HighLev}_{i}\\ &+ \beta_{3}\text{PostFinCrisis}_{t} \times \text{HighLev}_{i}+X_{i,j,t - 1}\\ &+ \sum \beta_{n}\text{PostFinCrisis}_{t} \times \text{Performance}_{i,j}+v_{j}+\epsilon_{c} \end{aligned} \] \[ \begin{aligned} \text{ContingencyR}_{c} &= \beta_{1}\text{PostCovid}_{t}+\beta_{2}\text{HighCovidExp}_{i}\\ &+ \beta_{3}\text{PostCovid}_{t} \times \text{HighCovidExp}_{i}+X_{i,j,t - 1}\\ &+ \sum \beta_{n}\text{PostCovid}_{t} \times \text{Performance}_{i,j}+\gamma_{t}+v_{j}+\epsilon_{c} \end{aligned} \]

• Innovation uncertainty shock: Industry technology diffusion
  • 29 disruptive technologies diffusion following Bloom et al. (2021)
  • Industry Technology Diffusion is the decile-ranked exposure of the firm’s industry to the 29 disruptive technologies. \[ \begin{aligned} \text{ContingencyR}_{c} =& \beta\text{Industry Technology Diffusion}_{j, t}\\ &+X_{i,j,t - 1}+\gamma_{t}+v_{j}+\epsilon_{c} \end{aligned} \]

Further evidence of dynamic contracting model

• Expected re-negotiation costs and the contingency-uncertainty relation
  • Dynamic contracting models: more costly to re-negotiate a new contract than amending actions ex-post conditional on the written contingency.
    • Product market rivals: more costly to re-negotiate;
    • More falmiliar contracting parties: less costly to re-negotiate;
    • More important contracting parties: less costly to re-negotiate.

• Simlar findings in contingency-innovation uncertainty relations

• Roughly drafted contingencies
  • More roughly drafted contingencies under high uncertainty according to the dynamic contracting models.

• No impact on length of normal action clauses under high uncertainty.

• Non-linearity
  • Could there be a threshold?

• Similar findings in innovation uncertainty

• Robustness tests
  • Firm-level measures of uncertainty
  • Alternative measures of industry earnings volatility
  • Alternative measures of innovation uncertainty
  • Controlling for more counterparty characteristics

Conclusion

• This paper documents robust evidence of a positive relation between contract contingencies and business uncertainty, which is consistent with predictions from the dynamic contracting models.

Potential contributions

• Test incomplete contract theories using a large sample of material product market contracts
  • Existing work tends to focus on smaller samples of product market contracts from specific industries (e.g., Costello, 2013; Krishnan and Mani, 2020).
• Offer the first empirical study of contract contingency and provide novel evidence of its determinants
  • This new contracting feature represents a major advancement from prior studies’ focus on the number of actions to proxy for “contract completeness”.

• Broaden the scope of empirical accounting research in contracting (which focuses heavily on financial contracts and executive compensation contracts)
  • Product market contracts are fundamentally different from financial contracts, as the former cover a broad spectrum of operating and investing activities while the latter focus on financing activities.